ML Platform Engineer

About the job Keep large language models serving reliably at scale — fast, observable, and always up. This is the production-engineering backbone of an AI platform: deployment, autoscaling, observability, and the SLOs that keep inference humming under real traffic. You'll join a small, senior team at an established enterprise software company building LLM-powered capabilities into its products. What you'll do: Own the reliability and scalability of LLM serving in production Build and run deployment, autoscaling, and orchestration on Kubernetes Instrument serving with real observability — time-to-first-token, tokens/sec, latency percentiles, error budgets Set and defend SLOs; load-test and harden the platform against traffic spikes Operate the serving stack (vLLM / Triton / TensorRT-LLM) as a dependable production system What you'll bring: Strong production / SRE / platform-engineering experience running services at scale Deep Kubernetes and cloud infrastructure skills Observability and reliability discipline (SLOs, monitoring, incident response) Comfort operating GPU-backed or ML workloads — a deep ML background is not required Solid software engineering fundamentals Nice to have: Experience serving ML/LLM models specifically Familiarity with inference frameworks (vLLM, Triton, TensorRT-LLM) Performance / load-testing background If you've kept high-scale systems alive and want to move into AI infrastructure, this is a clean on-ramp.